fix coding standard

adversarial/README.md

 # Advbox
 
 Advbox is a Python toolbox to create adversarial examples that fool neural networks. It requires Python and paddle.
+
+## How to use
+
+1. train a model and save it's parameters. (like fluid_mnist.py)
+2. load the parameters which is trained in step1, then reconstruct the model.(like mnist_tutorial_fgsm.py)
+3. use advbox to generate the adversarial sample.

adversarial/advbox/__init__.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """
    A set of tools for generating adversarial example on paddle platform 
 """

adversarial/advbox/attacks/base.py

@@ -7,6 +7,7 @@ import abc
 
 abstractmethod = abc.abstractmethod
 
+
 class Attack(object):
     """
     Abstract base class for adversarial attacks. `Attack` represent an adversarial attack

adversarial/advbox/attacks/gradientsign.py

@@ -6,6 +6,7 @@ import numpy as np
 from collections import Iterable
 from .base import Attack
 
+
 class GradientSignAttack(Attack):
     """
     This attack was originally implemented by Goodfellow et al. (2015) with the
@@ -22,10 +23,11 @@ class GradientSignAttack(Attack):
         gradient_sign = np.sign(gradient) * (max_ - min_)
 
         if not isinstance(epsilons, Iterable):
-            epsilons = np.linspace(0, 1, num = epsilons + 1)
+            epsilons = np.linspace(0, 1, num=epsilons + 1)
 
         for epsilon in epsilons:
-            adv_img = image_batch[0][0].reshape(gradient_sign.shape) + epsilon * gradient_sign
+            adv_img = image_batch[0][0].reshape(
+                gradient_sign.shape) + epsilon * gradient_sign
             adv_img = np.clip(adv_img, min_, max_)
             adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
             #print("pre_label="+str(pre_label)+ " adv_label="+str(adv_label))
@@ -33,4 +35,5 @@ class GradientSignAttack(Attack):
                 #print(epsilon, pre_label, adv_label)
                 return adv_img
 
+
 FGSM = GradientSignAttack

adversarial/advbox/models/base.py

@@ -6,8 +6,8 @@ import abc
 
 abstractmethod = abc.abstractmethod
 
-class Model(object):
 
+class Model(object):
     """
     Base class of model to provide attack.
 

adversarial/advbox/models/paddle.py

@@ -7,6 +7,7 @@ from paddle.v2.fluid.framework import program_guard
 
 from .base import Model
 
+
 class PaddleModel(Model):
     """
     Create a PaddleModel instance.
@@ -30,9 +31,7 @@ class PaddleModel(Model):
                  channel_axis=3,
                  preprocess=None):
         super(PaddleModel, self).__init__(
-            bounds=bounds,
-            channel_axis=channel_axis,
-            preprocess=preprocess)
+            bounds=bounds, channel_axis=channel_axis, preprocess=preprocess)
 
         if preprocess is None:
             preprocess = (0, 1)
@@ -48,7 +47,8 @@ class PaddleModel(Model):
 
         # gradient
         loss = self._program.block(0).var(self._cost_name)
-        param_grads = fluid.backward.append_backward(loss, parameter_list=[self._input_name])
+        param_grads = fluid.backward.append_backward(
+            loss, parameter_list=[self._input_name])
         self._gradient = param_grads[0][1]
 
     def predict(self, image_batch):
@@ -63,14 +63,11 @@ class PaddleModel(Model):
         feeder = fluid.DataFeeder(
             feed_list=[self._input_name, self._logits_name],
             place=self._place,
-            program=self._program
-        )
+            program=self._program)
         predict_var = self._program.block(0).var(self._predict_name)
-        predict = self._exe.run(
-            self._program,
+        predict = self._exe.run(self._program,
                                 feed=feeder.feed(image_batch),
-            fetch_list=[predict_var]
-        )
+                                fetch_list=[predict_var])
         return predict
 
     def num_classes(self):
@@ -96,11 +93,9 @@ class PaddleModel(Model):
         feeder = fluid.DataFeeder(
             feed_list=[self._input_name, self._logits_name],
             place=self._place,
-            program=self._program
-        )
+            program=self._program)
 
-        grad, = self._exe.run(
-            self._program,
+        grad, = self._exe.run(self._program,
                               feed=feeder.feed(image_batch),
                               fetch_list=[self._gradient])
         return grad